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Meeting Number:   29

October 23, 2012


Topic

Data-Driven Analysis and Fusion of Medical Imaging Data


Speaker

Dr. Tulay Adali
Professor
Machine Learning for Signal Processing Lab
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County
1000 Hilltop Circle
Baltimore, MD 21250


Date

Tuesday, October 23, 2012


Time

6:00 PM:   Snacks
6:30 PM:   Talk begins


Location

National Electronics Museum (NEM)
1745 W. Nursery Road, Linthicum, MD 21090
410-765-0230
http://www.nationalelectronicsmuseum.org


Registration

https://meetings.vtools.ieee.org/meeting_view/list_meeting/14607

To register for this meeting, go to the above link. Click on the ‘Click Here to Register’ button. You need to fill in the following information: Name, Member Number (if IEEE member), City, Country, State/Province, E-mail Address. After you fill in the information, click on the ‘Register’ button to register.


Please Respond To

ronald_aloysius@ieee.org

Please respond to ronald_aloysius@ieee.org if you are planning to join us afterwards for dinner so I can make reservations. Only the speaker’s dinner is paid for. The rest of us need to pay our own way.


Abstract

Data-driven methods such as independent component analysis (ICA) have proven quite effective for the analysis of functional magnetic resonance (fMRI) data and for discovering associations between fMRI and other medical imaging data types such as electroencephalography (EEG) and structural MRI data. Without imposing strong modeling assumptions, these methods efficiently take advantage of the multivariate nature of fMRI data and are particularly attractive for use in cognitive paradigms where detailed a priori models of brain activity are not available.

This talk reviews major data-driven methods that have been successfully applied to fMRI analysis and fusion, and presents examples of their successful application for studying brain function in both healthy individuals and those suffering from mental disorders such as schizophrenia.


Biography

Tulay Adali received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, the same year where she currently is a Professor in the Department of Computer Science and Electrical Engineering. She has held visiting positions at Ecole Superieure de Physique et de Chimie Industrielles, Paris, France, Technical University of Denmark, Lyngby, Denmark, Katholieke Universiteit, Leuven, Belgium, University of Campinas, Brazil, and University of Newcastle, Australia.

Prof. Adali assisted in the organization of a number of international conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE International Workshop on Neural Networks for Signal Processing (NNSP), and the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). She was the General Co-Chair, NNSP (2001--2003); Technical Chair, MLSP (2004--2008); Program Co-Chair, MLSP (2008 and 2009), 2009 International Conference on Independent Component Analysis and Source Separation; Publicity Chair, ICASSP (2000 and 2005); and Publications Co-Chair, ICASSP 2008.

Prof. Adali chaired the IEEE SPS Machine Learning for Signal Processing Technical Committee (2003--2005); Member, SPS Conference Board (1998--2006); Member, Bio Imaging and Signal Processing Technical Committee (2004--2007); and Associate Editor, IEEE Transactions on Signal Processing (2003--2006), Elsevier Signal Processing Journal (2007--2010). She is currently Chair of the MLSP Technical Committee and serving on the Signal Processing Theory and Methods Technical Committee; Associate Editor, IEEE Transactions on Biomedical Engineering and Journal of Signal Processing Systems for Signal, Image, and Video Technology; Senior Editorial Board member, IEEE Journal of Selected Areas in Signal Processing.

Prof. Adali is a Fellow of the IEEE and the AIMBE, and the recipient of a 2010 IEEE Signal Processing Society Best Paper Award and an NSF CAREER Award. She is an IEEE Signal Processing Society Distinguished Lecturer for 2012 and 2013. Her research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis.



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